International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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QuantumVision: A Hybrid Quantum-Classical Framework for 3D Image Reconstruction from Partial Views

Author(s) Mr. Manoj Swagath Adapa, B. Abhinav, I. Vinay Kumar, Y. Ramu, B. Krishna Kumar, Battula Balnarsaiah
Country India
Abstract Building a 3D scene from a limited number of images remains a challenging problem in computer vision, particularly for applications such as augmented reality, robotics, medical imaging, and autonomous navigation. Existing deep learning models like LRM and vFusion3D achieve high-quality reconstructions but require significant computational resources and long inference times, making them difficult to deploy on edge devices. This research introduces QuantumVision, a hybrid quantum-classical framework that integrates quantum image-processing techniques into a traditional 3D reconstruction pipeline. The framework consists of three modular components: quantum-assisted preprocessing, amplitude-based feature encoding, and a variational quantum optimization stage. These modules are designed to integrate with classical workflows while leveraging the potential advantages of Noisy Intermediate-Scale Quantum (NISQ) devices. Experimental simulations using Qiskit demonstrate improved feature extraction, edge detection, and reconstruction fidelity compared to classical baselines. The proposed approach highlights the potential of hybrid quantum computing to enhance computational efficiency and accuracy in partial-view 3D reconstruction tasks while remaining compatible with current quantum hardware limitations.
Keywords 3D reconstruction, quantum image processing, hybrid quantum-classical framework, neural radiance fields, NISQ devices
Field Computer Applications
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-19
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.71864
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E-ISSN 2582-2160
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